System and method for central association and tracking in passive coherent location applications

ABSTRACT

A system and method for central association and tracking for PCL applications is disclosed. Detection reports are received at a target tracking processing system. The detection reports include measurements correlating to line tracks associated with target echoes in earlier processing operations. In addition, other information, such as parameters and observables, are received by the target track processing system. The target track processing system performs a line track association function and a track filtering function on the line tracks according to the measurements within the detection reports. These operations also predict and estimate target parameters for tracking. Target parameters are extrapolated from the propagated and updated target tracks, and fed to a display for a user, or back into the PCL system for further processing.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application is a Continuation of U.S. application Ser. No.10/136,441 filed May 2, 2002, now U.S. Pat. No. ______ (unknown) whichclaims benefit of U.S. Provisional Patent Application No. 60/288,492entitled “System and Method for Central Association and Tracking for PCLApplications,” filed May 4, 2001, which are hereby incorporated in theirentirety by reference.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to a passive coherent location(“PCL”) radar system and method, and more particularly, to a system andmethod for associating a line track with a target and tracking thetarget in PCL radar applications.

[0004] 2. Discussion of the Related Art

[0005] PCL radar systems may be represented by a multistatic radarsystem. A multistatic radar system has a number of receivers that areseparated from one or more transmitters. The radiated signal from atransmitter arrives at a receiver via several separate paths. One pathmay be a direct path from the transmitter to the receiver, and the otherpath may be a target path that includes an indirect path from thetransmitter to a target to the receiver. Measurements may include atotal path length, or transit time, of the target path signal, the angleof arrival of the target path signal, and the frequency of the directand target path signals. A difference in frequency may be detected ifthe target is in motion according to a doppler effect.

[0006] Knowledge of the transmitted signal is desirable at the receiverif information is to be extracted from the target path signal. Thetransmitted frequency is desired to determine the doppler frequencyshift. A time or phase reference also is desired if the total scatteredpath length is to be determined. The frequency reference may be obtainedfrom the direct signal. The time reference also may be obtained from thedirect signal provided the distance between the transmitter and thereceiver is known.

[0007] Multistatic radar systems may be capable of determining thepresence of a target within the coverage of the radar, the location ofthe target position, and a velocity component, or doppler, relative tothe radar. The process of locating the target position may include ameasurement of a distance and the angle of arrival. The measurement ofdistance relative to the receiving site may include both the angle ofarrival at the receiving site and the distance between transmitter andreceiver. If the direct signal is available, it may be used as areference signal to extract the doppler frequency shift.

[0008] In PCL radar systems, transmitters may be known as illuminators.Illuminators may be wideband sources of opportunities that includecommercial frequency modulated (“FM”) broadcast transmitters and/orrepeaters, commercial high-definition television (“HDTV”) broadcasttransmitters and/or repeaters, and the like. Techniques for widebandsignal pre-detection processing and co-channel interference mitigationexist. Approaches may include an array of antennas used to receive thesource of opportunity to be exploited, such as the primary illuminator,and any other co-channel signals present in the environment.

[0009] PCL systems may receive a multitude of direct and reflectedsignals from several different transmitters. The signals should beidentified and associated with the appropriate target. Further, severaltargets may be scattering signals in different locations. The differentsignals and their measurement data should be associated with theappropriate target. If the target does not exist, then a new trackingmay have to be implemented for the target. Conversely, old trackingsshould be eliminated from the system if updates are no longer beingreceived. More efficient and expedient measurement data association mayimprove target tracking in PCL systems.

SUMMARY OF THE INVENTION

[0010] Accordingly, the present invention is directed to PCLapplications and signal processing. Thus, a system and method forcentral association and tracking within PCL applications is disclosedherein.

[0011] According to an embodiment, a method for associating a line trackwith a target for a passive coherent location system is disclosed. Themethod includes receiving a detection report having the line track thatcorresponds to the target. The method also includes computing a targetstate and state covariance for measurements of the line track. Themethod also includes scoring the line track according to the targetstate and the state covariance. The method also includes assigning theline track to a target track according to the scoring.

[0012] According to another embodiment, a method for associating andtracking target data within a passive coherent location system isdisclosed. The target data includes measurements. The method includescomputing a target state and state covariance from the measurements. Themethod also includes assigning a line track correlating to the targetdata to a target track according to the target state and the statecovariance. The method also includes initializing the target track. Themethod also includes initializing a filter according to the target stateand the state covariance. The method also includes tracking the targettrack with the filter. The method also includes extrapolating the targetdata from the target track.

[0013] According to another embodiment, a method for associating a linetrack to a target track from target tracking operations within a passivecoherent location system is disclosed. The method also includesdetermining a candidate association combination for the line track. Themethod also includes applying at least one gate to the candidateassociation combination. The method also includes estimating a targetstate and a state covariance for the line track. The method alsoincludes computing a score for the candidate association combinationaccording to the target state and the state covariance. The method alsoincludes assigning the line track to a target track according to thescore.

[0014] According to another embodiment, a method for filtering a targettrack correlating with a detection report having measurements associatedwith a target within a passive coherent location system is disclosed.The method includes computing corrections for a target state and statecovariance for the detection report. The method also includes updatingthe target state and state covariance with the corrections. The methodalso includes propagating the target track with the updated target stateand the updated state covariance.

[0015] According to another embodiment, a system for estimating targetparameters for a target is disclosed. The system includes detectionreports comprising measurements. The system also includes a line trackassociation function to associate a line track correlating to thedetection reports to a target track. The system also includes a trackfiltering function to propagate the target track according to themeasurements. The system also includes a target extrapolation functionto calculate the target parameters from the target track and themeasurements.

[0016] According to another embodiment, a system for associating a linetrack to a target track is disclosed. The line track correlates to atleast one detection report. The system includes a nonlinear leastsquares batch estimator to compute a target state and state covariancefor measurements from the at least one detection report and to score acandidate associate combination for the line track. The system alsoinclude a line track assignment function to assign the line trackaccording to the score for the candidate associate combination.

[0017] According to another embodiment, a system for track filtering atarget track is disclosed. The target track is associated with a linetrack from at least one detection report. The system includes a filterto compute corrections to a target state and state covariance to updatethe target track using a means for computing measurement residuals andpartial derivatives of measurements from the detection report. Thesystem also includes a validity check function to check the updatedtarget track using a velocity magnitude component and an accelerationmagnitude component.

[0018] Additional features and advantages of the invention will be setforth in the disclosure that follows, and in part will be apparent fromthe disclosure, or may be learned by practice of the invention. Theobjectives and other advantages of the invention will be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which is included to provide furtherunderstanding of the invention and is incorporated in and constitutes apart of this specification, illustrates embodiments of the presentinvention and together with the description serves to explain theprinciples of the invention.

[0020] In the drawings:

[0021]FIG. 1 illustrates a block diagram of a radar system, a target,and transmitters in accordance with an embodiment of the presentinvention;

[0022]FIG. 2 illustrates a block diagram of components for a passivecoherent location system in accordance with an embodiment of the presentinvention;

[0023]FIG. 3A illustrates a block diagram of a system for centralassociation and tracking targets within a PCL system in accordance withan embodiment of the present invention;

[0024]FIG. 3B illustrates an overview of the geometry used in thecalculation of bistatic time delay and bistatic Doppler in accordancewith an embodiment of the present invention;

[0025]FIG. 3C illustrates an angle-of-arrival of an incoming signal inaccordance with an embodiment of the present invention.

[0026]FIG. 4 illustrates a line track association function in accordancewith an embodiment of the present invention;

[0027]FIG. 5 illustrates a flowchart for line track associationoperations in accordance with an embodiment of the present invention;

[0028]FIG. 6 illustrates a flowchart for line track associationoperations in accordance with another embodiment of the presentinvention;

[0029]FIG. 7 illustrates a flowchart for initializing and scoring targettracks in accordance with an embodiment of the present invention;

[0030]FIG. 8 illustrates a flowchart for assigning line tracks inaccordance with an embodiment of the present invention;

[0031]FIG. 9 illustrates a block diagram for assigning line tracks inaccordance with an embodiment of the present invention;

[0032]FIG. 10 illustrates a flowchart for filtering detection reports inaccordance with an embodiment of the present invention;

[0033]FIG. 11 illustrates a flowchart for performing validity checks fortargets in accordance with an embodiment of the present invention; and

[0034]FIG. 12 illustrates a flowchart for associating a coasting targetin accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0035] Reference will now be made in detail to the preferred embodimentsof the present invention, examples of which are illustrated in theaccompanying drawings.

[0036]FIG. 1 depicts a block diagram of a radar system, a target andtransmitters in accordance with an embodiment of the present invention.Radar detection system 10 includes a PCL system 100 tracking one or moretargets of interest 150 using a plurality of transmitters 110, 112, and114. PCL system 100 represents a family of multi-static wide area targetsurveillance sensors. PCL system 100 exploits continuous wave (“CW”)electromagnetic energy, often from sources of opportunity that may beoperating for other purposes. Sources of opportunity may includetelevision broadcast stations and FM radio stations. Preferably, PCLsystem 100 may receive transmissions from a plurality of uncontrolledtransmitters, also known as sources of opportunity, 110, 112, and 114.An uncontrolled transmitter pertains to transmitters that are not underthe direct control of the receiver. More preferably, transmitters 110,112, and 114 may be wideband sources of opportunity that includecommercial FM broadcast transmitters and/or repeaters and commercialHDTV TV broadcast transmitters and/or repeaters. Transmitters 110, 112,and 114, however, are not limited to these sources of opportunity andmay include any device, system or means to transmit uncontrolledsignals.

[0037] Transmitters 110, 112, and 114 may transmit widebandelectromagnetic energy transmissions in all directions. Some of thesetransmissions are reflected by one or more targets of interest 150 andreceived by PCL system 100. For example, reflected transmission 130 maybe reflected by target 150 and received by PCL system 100. Further, withregard to transmitter 114, reference transmission 140 is receiveddirectly by PCL system 100. PCL system 100 may compare referencetransmission 140 and reflected transmission 130 to determine positionalinformation about one or more targets of interest 150. Referencetransmission 140 also may be known as a direct path signal. Reflectedtransmission 130 also may be known as a target path signal. Positionalinformation may include any information relating to a position of target150, including location, velocity, and acceleration from determining atime difference of arrival (“TDOA”), a frequency difference of arrival(“FDOA”) and an angle of arrival (“AOA”).

[0038]FIG. 2 depicts a block diagram of a passive coherent locationsystem in accordance with an embodiment of the present invention. PCLsystem 100 may include antenna subsystem 200, analog to digitalconverter (“ADC”) subsystem 220, processing subsystem 240, and outputdevice 260. Antenna subsystem 200 receives electromagnetic energytransmissions, including reflected transmission 130 and referencetransmission 140 of FIG. 1, with at least one antenna. Preferably,antenna subsystem 200 is an antenna array. ADC subsystem 220 receivesthe signal outputs of antenna subsystem 200 at its input and outputsdigital samples of the signals by sampling the signals at a samplingrate and forming a digital waveform using the magnitude for the analogsignal at each sampling interval. Processing subsystem 240 receives theoutput of assembly subsystem 220 and processes the signals formeasurement data, tracking, target updates, and the like. Output device260 receives the processing result and displays the output of processingsubsystem 240.

[0039]FIG. 3A depicts a system for central association and trackingtargets within a PCL system in accordance with an embodiment of thepresent invention. Target track processing system 300 provides centralassociation and tracking for PCL applications by receiving inputs andproducing an output to display 344 and to additional PCL signalprocessing function 346. Target track processing system 300 estimatesposition, velocity, and acceleration for targets detected by at leastone transmitter. The position may be 3-dimensional under certainrestrictions.

[0040] Target track processing system 300 may receive a stream ofdetection blocks 302 from a line tracker. Detection blocks 302 maycontain detection reports that are identified by line trackidentification number, illuminator identification, and time ofdetection. Further, each detection report may include a state parameterthat specifies that status of the line track. Inputs also may includeparameters 304. Parameters 304 may used to initialize target tracks.Parameters 304 may include the input parameters disclosed in Table 1below. The input parameters of Table 1 may specify each receiver andeach illuminator to be processed by target tracking processing system300. TABLE 1 Parameter Description Default Value Units rcv_lat Latitudeof the receiver — deg rcv_lon Longitude of the receiver — deg rcv_altAltitude of the receiver — m rcv_bsaz Azimuth of the receiver boresight230.0 deg with respect to North ilm_lat Latitude of the illuminator —deg ilm_lon Longitude of the illuminator — deg ilm_alt Altitude of theilluminator — m ilm_freq Frequency of the illuminator's trans- — Hzmitted signal

[0041] Target tracking processing system 300 produces output 342. Output342 is a stream of target data blocks for the current coherentprocessing interval. Output 342 may be received by display 344. Display344 may include display software and associated hardware to display thetargets to a user. Further, output 342 may be received by PCL signalprocessing function 346, which feeds output 342 back to earlier elementsof the PCL signal processing chain. Preferably, PCL signal processingfunction 346 feeds output 342 to processing elements for detection andfeature extraction. Table 2 discloses a preferable list of outputparameters for each target data block of output 342. TABLE 2 ParameterDescription Units trg_ID Target ID pure lt_ID Line track IDs associatedwith the target (includes pure illuminator IDs) trg_sta Target state(i.e.; Updated, Coasting, New) pure trg_lat Latitude of the target degtrg_lon Longitude of the target deg trg_crs Course deg trg_spd Speed m/strg_clr Climb rate m/s trg_slr Slant range m trg_gcr Great circle rangem trg_brg Bearing deg trg_brr Bistatic range rate per link m/s trg_altAltitude m trg_snr Signal to noise ratio per link dB trg_age Age sectrg_pwr Signal power per link dBm

[0042] As shown, target tracking processing system 300 comprises threefunctions. Line track association function 320 makes all linetrack-target track assignments, handles the initialization of all newtarget tracks and reinitialization of existing tracks if warranted, andmonitors the quality of the assignments by dissolving those assignmentsthat become inconsistent. Track filtering function 330 utilizes anextended Kalman filter to track the position, velocity, and accelerationof each target in 2-dimensions, or, if all conditions are met, in3-dimensions. The initial state and covariance of track filteringfunction 330 may be initialized by line track association function 320.Track filtering function 330 propagates each track incorporatingmeasurements and monitors the line track association to ensure the trackremains valid. Both line track association function 320 and trackfiltering function 330 are disclosed in greater detail below.

[0043] Target data extrapolation function 340 calculates the target datadesired for display 344 from target tracking processing system 300.Target data extrapolation function 340 generates a signal for eachtarget. The state vectors of position (“T”) and velocity (“T²”) are usedto calculate the parameters in output 342. The state vectors for thereceiver position (“R”) and velocity (“R²”) also are used. Each vectormay have three variables representing individual coordinates. Forexample, the target position vector may be represented as T=[x_(T),y_(T), z_(T)], while target velocity may be T²=[x_(T) ², y_(T) ², z_(T)²]. Target and receiver position locations may be described in theEast-North-Up (“ENU”) coordinate system, disclosed below. The Upcoordinate in the ENU coordinate system may be defined as the zenith oras normal to the local tangent plane where the tangent plane comprisesthe East and North coordinates.

[0044] Two types of state vectors and associated covariances may be usedin target tracking. The first is the updated state vector that isgenerated each time a target receives an update. The second is thepropagated state vector that is the extrapolated target trajectorycalculated for each coherent processing interval. The propagated statevectors and covariances may be the type of state vectors used for thecalculations disclosed in Table 3 below. Line track association function320 and track filtering function 330 seek to establish values or updatesfor the state vectors and covariances that are used in target dataextrapolation function 340. TABLE 3 PARAMETER CALCULATION Course${- {\tan^{- 1}\left( \frac{{\overset{.}{x}}_{T}}{{\overset{.}{y}}_{T}} \right)}}*{RTD}$

Speed$\sqrt{\left( {\overset{.}{x}}_{T} \right)^{2} + \left( {\overset{.}{y}}_{T} \right)^{2} + \left( {\overset{.}{z}}_{T} \right)^{2}}$

Climb Rate ${\overset{.}{z}}_{T}$

Slant Range$\sqrt{\left( x_{T} \right)^{2} + \left( y_{T} \right)^{2} + \left( z_{T} \right)^{2}}$

Great Circle Range $\begin{matrix}{{\overset{\_}{E}}_{RAD}*{\cos^{- 1}\left( \frac{T_{ECF} \cdot R_{ECF}}{{T_{ECF}}*{R_{ECF}}} \right)}} \\\begin{matrix}{{{where}:{\overset{\_}{E}}_{RAD}} = {{mean}\quad {earth}\quad {radius}}} \\{\quad {\cdot {= {{vector}\quad {dot}\quad {product}}}}} \\{\quad {T_{ECF}\quad {and}\quad R_{ECF}\quad {are}\quad {the}\quad {target}\quad {and}\quad {receiver}}} \\{\quad {{position}\quad {vectors}\quad {in}\quad {Earth}\text{-}{Centered}\text{-}{Fixed}}} \\{\quad {({ECF})\quad {coordinates}}}\end{matrix}\end{matrix}\quad$

Bearing${- {\tan^{- 1}\left( \frac{y_{T} - y_{R}}{x_{T} - x_{R}} \right)}}*{RTD}$

Bistatic Range −λ(fd_update) Rate where: X = the wavelength of theilluminator fd_update = the updated doppler measurement SNR Provided intarget data block Course Uncertainty $\begin{matrix}{{\frac{\sqrt{{\overset{2}{\sigma_{{\overset{.}{x}}_{T}}}*{\overset{.}{y}}_{T}^{2}} + {\sigma_{{\overset{.}{y}}_{T}}^{2}{\overset{.}{x}}_{T}^{2}}}}{{\overset{.}{x}}_{T}^{2} + {\overset{.}{y}}_{T}^{2}}*{RTD}};} \\{{{where}\text{:}\quad \sigma^{2}} = {variance}}\end{matrix}\quad$

Speed Uncertainty $\begin{matrix}\sqrt{\frac{{\overset{2}{\sigma_{{\overset{.}{x}}_{T}}}*{\overset{.}{x}}_{T}^{2}} + {\sigma_{{\overset{.}{y}}_{T}}^{2}*{\overset{.}{y}}_{T}^{2}} + {\overset{2}{\sigma_{{\overset{.}{z}}_{T}}}*{\overset{.}{z}}_{T}^{2}}}{{\overset{.}{x}}_{T}^{2} + {\overset{.}{y}}_{T}^{2} + {\overset{.}{z}}_{T}^{2}}} \\{{{where}\text{:}\quad \sigma^{2}} = {variance}}\end{matrix}\quad$

Climb Rate Uncertainty $\sigma_{{\overset{.}{z}}_{T}}$

[0045] Prior to implementing line track association function 320,certain values and additional parameters should be calculated. Thesevalues and their associated algorithms may be used by target trackingprocessing system 300 in determining target data. The values may be usedby any of the functions of target tracking processing system 300. First,reference frames 310 may be calculated. Target tracking processingsystem 300 may desire manipulation between three primary referenceframes. The disclosed reference frames and conversion between frameswill be referred in the following disclosure with relation to referenceframes 310. The following discussion discloses the reference frames 310and the coordinate system transformations.

[0046] The Earth Centered, Fixed (“ECF”) reference frame is a Cartesianreference frame defined at the geographic center of the earth. Theequator may define the primary plane of the system with the primary axispointing toward the Greenwich meridian. The ECF frame is attached to therotating Earth. All Earth rotation effects, however, may be neglected intarget tracking processing system 300.

[0047] The Geodetic (“GEO”) coordinate system defines locations on theEarth's surface with respect to a reference ellipsoid. The referenceellipsoid may be taken to be the ellipsoid of revolution that best fitsthe mean sea level. Target tracking processing system 300 may use thevalues disclosed in Table 4 below, along with the relationships betweenthe parameters. TABLE 4 Mean Equatorial Radius, r_(e) 6378137.0 m MeanPolar Radius, r_(p) 6356752.3141 m A Useful Constant, u 0.99330562$u = {\frac{r_{p}^{2}}{r_{e}^{2}} = \left( {1 - f} \right)^{2}}$

Eccentricity, e 0.08151919 e² = 1 − u = f(2 = f) Flattening coefficient,f f⁻¹ = 298.257223563

[0048] The Local Tangent (“ENU”) reference frame is a Cartesianreference frame defined at a point on the reference ellipsoid. The localtangent plane defines the primary plane of the system with the primaryaxis pointing in the local East direction. All target tracking and linetrack association functions for target track processing, includingvehicle extrapolation and Kalman filtering, are performed in the localtangent frame of the receiver. Target tracking processing system 300 mayperform functions in 2-dimensions or 3-dimensions.

[0049] For 2-dimensional target tracks, target tracking processingsystem 300 may incorporate a constant velocity with a straight and levelflight. The straight and level flight condition follows the surface ofthe Earth and not the tangent plane of the receiver. Therefore, thevertical components of position and velocity may be corrected for thecurvature of the Earth.

[0050] State dynamics 308 may disclose the target state vector,covariance matrix, and model for its dynamics. The model disclosed bystate dynamics 308 may be defined for the East, North, and Up componentsin the ENU local reference frame of the receiver. In the case of2-dimensional tracking, the target is given a specified altitude thatmay be corrected for the earth's curvature over the duration of itstrack. The target vehicle state dynamics 308 incorporates anexponentially correlated acceleration motion model.

[0051] Associated with the target state is a target state covariancematrix that reflects the uncertainty of the state values. The diagonalvalues of the covariance matrix represent the variance of the statevalues. The off-diagonal elements reflect the correlation between thestates. The effects of the exponential correlated acceleration modelshould be included in the extrapolation of the state covariance. Thecovariance matrix is propagated through time using the state transitionmatrix and the process noise matrix.

[0052] Observables 306 are measurement observables used to initializeand update a target's state. The observables disclosed here may becomputed for each coherent processing interval earlier in the PCL signalprocessing. Preferably, the observables are computed during detectionand feature extraction. Observables 306 may relate to calculation of thepartial derivatives of the observations with respect to the target statefor each observable. The partial derivatives are used during line trackassociation function 320 and track filtering function 330 of targettracking processing system 300. FIG. 3B depicts an overview of thegeometry used in the calculation of bistatic time delay and bistaticdoppler in accordance with an embodiment of the present invention.

[0053] The bistatic time delay of observables 306 reflects thedifference in time of travel between the illuminator and receiver alongthe indirect and direct paths. According to the following equation:$t_{d} = {\frac{1}{c}\left( {d_{I} + d_{R} - d_{D}} \right)}$

[0054] where c is the speed of light and the ranges between the objectsare expressed as the square root of the inner products or:

d _(I) ={square root}{square root over (r_(I)∘r_(I))}

d _(R) ={square root}{square root over (r_(R)∘r_(R))}

d _(D) ={square root}{square root over (r_(D)∘r_(d))}

[0055] The state estimation desires the calculation of partialderivatives with respect to the target state, x_(T). The partials of therange expressions with respect to the target state become:$\begin{matrix}{\frac{\partial d_{I}}{\partial x_{T}} = \frac{r_{I}}{d_{I}}} \\{\frac{\partial d_{R}}{\partial x_{T}} = \frac{r_{R}}{d_{R}}} \\{\frac{\partial d_{D}}{\partial x_{T}} = 0}\end{matrix}$

[0056] The expression for the partials of the bistatic time delaymeasurement with respect to the target state are: $\begin{matrix}{\frac{\partial t_{d}}{\partial x_{T}} = {\frac{1}{c}\left( {\frac{r_{I}}{d_{I}} + \frac{r_{R}}{d_{R}}} \right)}} \\{\frac{\partial t_{d}}{\partial{\overset{.}{x}}_{T}} = 0} \\{\frac{\partial t_{d}}{\partial a_{T}} = 0}\end{matrix}$

[0057] The bistatic doppler of observables 306 reflects the change infrequency between the observed signal and the transmitted signal from anilluminator. The measurement may desire the positions and velocities ofthe illuminator, receiver, and target. Thus, embodiments of the presentinvention may utilize moving transmitters and receivers, such as on anairborne platform or an ocean going vessel. Therefore, according to thefollowing equation:$f_{d} = {{- \frac{1}{\lambda}}\frac{\partial}{\partial t}\left( {d_{I} + d_{R}} \right)}$

[0058] where λ is the transmitting wavelength of the illuminator and theilluminator-target and receiver-target ranges are defined above. Thetime derivative of range (“range rate”) expressions may be:$\begin{matrix}{{\overset{.}{d}}_{I} = {\frac{1}{d_{I}}\left( {r_{I}\quad \circ \quad {\overset{.}{r}}_{I}} \right)}} \\{{\overset{.}{d}}_{R} = {\frac{1}{d_{R}}\left( {r_{R}\quad \circ \quad {\overset{.}{r}}_{R}} \right)}}\end{matrix}$

[0059] that results in an expression for doppler as:$f_{d} = {{- \frac{1}{\lambda}}\left( {\frac{r_{I}\quad \circ \quad {\overset{.}{r}}_{I}}{d_{I}} + \frac{r_{R}\quad \circ \quad {\overset{.}{r}}_{R}}{d_{R}}} \right)}$

[0060] The state estimation may desire the calculation of partialderivatives with respect to the target state, x_(T). The partials of therange rate expressions with respect to the target state may be$\begin{matrix}{\frac{\partial{\overset{.}{d}}_{I}}{\partial x_{T}} = {\frac{1}{d_{I}}\left( {{\overset{.}{r}}_{I} - {\frac{{\overset{.}{d}}_{I}}{d_{I}}r_{I}}} \right)}} \\{\frac{\partial{\overset{.}{d}}_{I}}{\partial{\overset{.}{x}}_{T}} = \frac{r_{I}}{d_{I}}} \\{\frac{\partial{\overset{.}{d}}_{R}}{\partial x_{T}} = {\frac{1}{d_{R}}\left( {{\overset{.}{r}}_{R} - {\frac{{\overset{.}{d}}_{R}}{d_{R}}r_{R}}} \right)}} \\{\frac{\partial{\overset{.}{d}}_{R}}{\partial{\overset{.}{x}}_{T}} = \frac{r_{R}}{d_{R}}}\end{matrix}$

[0061] The expression for the partials of the bistatic Dopplermeasurement with respect to the target state may be $\begin{matrix}{\frac{\partial f_{d}}{\partial x_{T}} = {- {\frac{1}{\lambda}\left\lbrack {{\frac{1}{d_{I}}\left( {{\overset{.}{r}}_{I} - {\frac{{\overset{.}{d}}_{I}}{d_{I}}r_{I}}} \right)} + {\frac{1}{d_{R}}\left( {{\overset{.}{r}}_{R} - {\frac{{\overset{.}{d}}_{R}}{d_{R}}r_{R}}} \right)}} \right\rbrack}}} \\{\frac{\partial f_{d}}{\partial{\overset{.}{x}}_{T}} = {{- \frac{1}{\lambda}}\left( {\frac{r_{I}}{d_{I}} + \frac{r_{R}}{d_{R}}} \right)}} \\{\frac{\partial f_{d}}{\partial a_{T}} = 0}\end{matrix}$

[0062] For television illuminators, doppler may be formed from frequencymeasurements of a target's echo and the illuminator carrier. Because theilluminator corresponding to target echo returns in a line track is notambiguous, the formed doppler measurements are associated with ahypothesis that is resolved in the line track association function 320.The constructed doppler for a hypothesis may be given by

f _(d) =f _(r)−(f _(c))_(r)=(f−f _(LO))−(f _(c) −f _(LO))

[0063] where f_(Lo) is the frequency of the oscillator (“Hz”), f_(c) isthe carrier frequency, and the subscript “r” identifies a frequency asrelative to the local oscillator.

[0064] The angle-of-arrival of an incoming signal is depicted by theangle θ in the body fixed frame of the antenna, as shown in FIG. 3C. Theoff-boresight angle reported to target tracking processing system 300 isthe complement of the angle-of-arrival, or θ_(ob)=θ−(π/2). Theangle-of-arrival calculation may desire the position of the target andthe orientation of the antenna array left hand axis, or${\cos \quad \theta} = {\frac{r_{R}\quad \circ \quad {lh}}{d_{R}}.}$

[0065] where lh is the left hand axis unit vector. The state estimationmay desire the calculation of partial derivatives with respect to thetarget state, x_(T). The partials of the angle-of-arrival expressionswith respect to the target state are$\frac{\partial\theta}{\partial x_{T}} = {\frac{1}{d_{R}\quad \sin \quad \theta}\left( {{\cos \quad \theta \quad \frac{x_{R}}{d_{R}}} - {1h}} \right)}$

[0066] The bistatic time delay, bistatic doppler, and angle-of-arrivalof observables 306 correlate to the time delay, doppler, andangle-of-arrival disclosed with reference to FIGS. 1 and 2. Targettracking processing system 300, however, uses these values, as disclosedbelow. The state expression and partials are used by target trackingprocessing system 300 to update and propagate the vectors, and,therefore, are disclosed with reference to FIGS. 3A, 3B, and 3C.

[0067]FIG. 4 depicts a line track association function in accordancewith an embodiment of the present invention. FIG. 4 depicts line trackassociation function 400 that correlates to line track associationfunction 320 of FIG. 3. Line track association function 400 discloses apreferred embodiment of line track association function 320. Line trackassociation function 320, however, is not limited by the embodimentsdisclosed by FIG. 4. Line track association function 400 seeks to allline track-to-target track assignments, to handle the initialization ofall new target tracks and reinitialization of existing tracks ifwarranted, and to monitor the quality of the assignments by dissolvingthose assignments that become inconsistent. Gating operations may beused to reduce the number of candidate assignments to be scored andevaluated by line tracking association function 400. The candidateassignments passing the gates are scored and provided as input to theassignment algorithm that makes the assignments based on the scores. Atthe current time, t, the assignment algorithm is applied prior to theKalman filter's measurement update at time t in track filtering function330.

[0068] Prior to line track association, certain conditions may be met toprocess the line track in a more efficient manner. Modulation line testfunction 404 receives line tracks 402. All FM line tracks should passthis test before line tracks 402 are allowed to participate in the linetrack association process. Modulation line test function 404 determinesif the line track of line tracks 402 was generated by a blade modulationline, and, if so, to prevent its use in line track association. A blademodulation line may be associated with rotor blades of an aircraft orvehicle, and may be known to one skilled in the art. Modulation linetest function 402 looks at the delta-delay time-series history obtainedfrom integrated doppler and from delay differences. If the line track ofline tracks 402 is a modulation line characterized by a doppler shiftrelative to the doppler due to body motion, the difference between thetwo delta-delay sequences may evolve with a linear runoff, or slope. Bydetecting the slope, a blade modulation line track is identified and maybe removed from further line track association operations.

[0069] Buffer 406 may buffer detection reports of line tracks 402 afterthe modulation line test. Buffer 406 preferably is a first-in, first-outbuffer for detection reports that are associated for each unique linetrack. Buffer 406 may mark the line track for the correlating detectionreports as “available for line track association” when a minimum numberof detection reports, or N_(BF), have been accumulated in buffer 406. Insubsequent line track association operations, line track associationfunction 400 may consider those line tracks that have been marked forfurther processing. The size of the queue for buffer 406 may beindependent of N_(BF) but, preferably, the size may be equal to orgreater than N_(BF). All of the detection reports of line tracks 402 inthe queue for buffer 406 may be used in the scoring computation of linetrack association function 400, as disclosed below. For televisionsignals, the preferable buffer size is 1.5 N_(BF).

[0070] Detection reports of line tracks 402 may be added to buffer 406if the line track state is 1-3 within the queue. The line track statecorrelates to the number of detection reports in buffer 406 correlatingto the particular line track. When line track state 4 is reached, thenline track termination is enacted. Buffer 406 performs certainhousekeeping operations to remove its association with any currentlyactive target track and to set the corresponding target track state to“coast” state if the target track has no remaining associated linetracks as a result of the line track removal from buffer 406 for furtherline track association operations.

[0071] Line track association function 400, after modulation testing andbuffering, may consider specified candidate association combinations(“CACs”). For example, there may be three types of CACs. The first CACmay be a current track I with line track j, or TL(I,j). The second CACmay be a new track from line tracks I and j, or LL(I,j). The third CACmay be a new track from line track I, or L(i). Processing within linetrack association function 400 may be restricted to those line tracksthat are marked available for line track association, are not assignedcurrently to a target track, or are assigned to a target track that hasnever had any other line track assignments, and have a current detectionreport. Further, line track association operations may be restricted tothose unassigned line tracks from link having TDOA measurements, such aslinks with television illuminators to the TL(I,j) type. Line trackprocessing may be restricted to optimize the functions within line trackassociation function 400. These restrictions, however, may not beimplemented, and all line tracks may be considered for line trackassociation processing. Moreover, additional or different restrictionsmay apply to the line tracks to optimize line track associationoperations.

[0072] Three passes may be performed through line track associationfunction 400. These “passes” may include gating, scoring and assignmentalgorithms using unassigned FM line tracks. In the first pass, theTL(I,j) combinations may be processed. At the end of this pass, thoseline tracks may be removed that have been assigned during this pass fromfurther consideration. In the second pass, the LL(i.j) combinations maybe processed by line track association function 400. At the end of thispass, those line tracks may be removed that have been assigned duringthis pass from further consideration. In the third pass, the remainingL(i) CACs may be processed. A fourth pass may be made to process thenewly updated TL(I,j) combinations, but now considering those unassignedline tracks without TDOA measurements, such as those correlating totelevision illuminators. The gating, scoring, assignment, and new trackinitialization algorithms within line track association function 400 aredisclosed in greater detail below.

[0073] Television frequency line track hypothesis function 408 mayresolve those line tracks where the illuminator associated with afrequency line track from a television radio-frequency channel isassumed to be ambiguous whenever multiple television illuminators havebeen identified with the channel. To handle this scenario, eachfrequency line track may be associated with an illuminator hypothesisfor each of the identified television illuminators. The ambiguity isassumed to be resolved when the line track under a particularilluminator has been successfully associated with an existing target.Preferably, only TL(I,j) combinations are considered. When this occurs,the transmitter identification field may be updated in all detectionreports associated with the line track.

[0074] The range rate measurements may be constructed for eachhypothesis because the hypothesis should depend on the location andcarrier frequency of the illuminator associated with the hypothesis. Theconstruction may be as follows. Let n=(tid, rid) be the link indexreferring to transmitter node “tid” and receiver node “rid”. Aparticular TL(I,j) may be considered such as target T_(i) and televisionfrequency line track L_(j) at time t_(k) under hypothesis n. For thishypothesis, the constructed range rate measurement for the line trackand the predicted range rate measurement for the target may be given by:

y _(jn)=−λ_(n)└(f _(r))_(j)−(f _(r,c))_(n)┘

ŷ _(in) ={overscore (V)} _(i) ·{overscore (B)} _(in) ≡{overscore (V)}_(i)·({circumflex over (r)} _(rx,in) +{circumflex over (r)} _(tx,in))

[0075] where (f_(r))_(j) is the relative frequency measurement from linetrack j, (f_(r,c))_(n) is the relative frequency of the carrier underhypothesis n, XX is the velocity vector for target “I”, and {overscore(V)}_(i); is the bistatic vector associated with target I underhypothesis n. The bistatic vector, {overscore (B)}_(in), may be definedas the sum of the unit vectors pointing from receiver-to-target andilluminator-to-target. This hypothesis may desire that the associatedcarrier line track has a relative frequency measurement for the carrier.The residual measurement may be defined as δy_(ijn)=y_(jn)−ŷ_(jn).

[0076] Gating tests may be applied to this residual measurement and itsassociated covariance matrix S disclosed below.

[0077] Gates 410 may be applied prior to scoring in order to rejectunlikely CACs from further processing. Gates 410 seek to reduce theprocessing load on line track association function 400. Any CAC failinga gate of gates 410 may be removed at the earliest opportunity toincrease processing efficiency and to reduce extraneous line trackprocessing. Gates 410 may be known as pre-scoring gates. Gates 410 mayinclude a link gate that allows at most one line track from any link ina CAC. A link gate of gates 410 may remove CACs having multiple linetracks from a common link from further processing during the currentpass of the line track association operations.

[0078] Gates 410 also may include a normalized innovations squared gatethat applies the normalized innovations squared gate for the TL(I,j)passes. The normalized innovations squared gate of gates 410 may utilizethe two-dimensional state vector and covariance of T(i) and themeasurement data in the buffered detection reports for L(j) in thecomputation for passing the line tracks. The normalized innovationssquared gate of gates 410 may incorporate the following steps inevaluating the line tracks. First, the doppler residuals, or y, and thedoppler partials, or H, may be computed for the line track as disclosedabove. Next, the measurement predicted covariance matrix S may becomputed as follows:

S=H{overscore (P)}H ^(T) +R

[0079] where {overscore (P)} is the a priori two-dimensional statecovariance from the existing target, H is the measurement partial matrixfor the line track, and R is the measurement noise matrix for the linetrack. Next, the normalized innovations squared ε may be computed andthe following gating criterion applied:

ε=y ^(T) S ⁻¹ y<γ

[0080] where γ may be the configurable number of sigmas squared to beused for gating the measurement type of interest. These steps may berepeated for time delay Doppler shift, and off-boresight angle, if theyare available.

[0081] Gates 410 may include a two-dimensional position wedge gate. Thetwo-dimensional position wedge gate of gates 410 may be applied toLL(I,j) combinations that have bistatic range and off-boresightmeasurements. The wedge gate pass criteria may be as follows:$\begin{matrix}{{{\theta_{i} - \theta_{j}}} \leq \eta_{\theta}} \\{{{R_{i} - R_{j}}} \leq \eta_{R}} \\{{{where}\quad R} = \frac{{.5}{r_{b}\left( {r_{b} + {2R_{d}}} \right)}}{r_{b} + {\left( {1 - {\cos \quad \gamma}} \right)\quad R_{d}}}}\end{matrix}$

[0082] and where theta is the cone angle-of-arrival measurement, R isthe range of the scattered signal target-to-receiver, R_(d) is thedirect path from illuminator to target, gamma is the angle between thereceiver-to-target and receiver-to-illuminator rays, and r_(b) is thebistatic range measurement. The gating tests of gates 410 may use theaverage of the bistatic range and angle measurements over the timeinterval common to both L(i) and L(j), where L(i) and L(j) are linetracks.

[0083] After applying gates 410, scoring and track initializationfunction 412 may initialize two-dimensional target tracks using a batchnonlinear least squares (“NLS”) operation and computes a score based onthe batch fit. Scoring and track initialization function 412 may be acallable module. The operations of scoring and track initializationfunction 412 estimates an initial state vector and covariance matrixbased on the buffered detection reports from one or more line tracks.Scoring and track initialization function 412 may perform two services.Scoring and track initialization function 412 may compute scores forCACs and also provide track initialization data. The trackinitialization data may be used when new tracks are formed or old tracksare reinitialized.

[0084] Scoring and track initialization function 412 incorporates NLSbatch estimator 414 to compute a score “s” for a proposed association ofa television line track with an existing target track. NLS batchestimator 414 computes a target state and state covariance for themeasurements from the detection reports in buffer 406 of one or moreline tracks. These computations may be forwarded to track filteringfunction 330. NLS batch estimator 414 also computes a score using an NLSalgorithm. Preferably, NLS batch estimator 414 is incorporated withinscoring and track initialization function 412. The operations andfunctions of scoring and track initialization function 412 and NLS batchestimator 414 are disclosed in greater detail below with reference toFIG. 6.

[0085] After scoring, gates 416 may be applied. Gates 416 may be knownas post-scoring gates. Post-scoring gates may test the normalizedchi-square score against an user supplied threshold. Gates 416 maydiscard those CACs that have a score that fails the gate criterion, ors≦ε_(ncs). A second test of gates 416 may consider the closeness of thetelevision line track to the existing target track. This test may besuccessful if the mean square distance is less than the square of theconfigurable RMS association gate, or S₂<η_(rmsag) ². If this testfails, the fit may be aborted and the proposed association may berejected. Thus, gates 416 may optimize further the line trackassociation operations of line track association function 400 byremoving those line track associations that fail the scoring criterionspecified by a user.

[0086] After initialization of a single link new target in scoring andtrack initialization function 412, track initialization check function418 may check that the normalized chi-square score satisfies therelationship s≦ε_(NCS) _(—SNGL) . If the check fails, then delete thenew target track and pass its associated line track on so it may try toinitialize at the next coherent processing interval. For statereinitialization from one to two FM links, initialization check function418 may ensure that the velocity and acceleration magnitudes satisfy therelationships:

{square root}{square root over (v)}∘{right arrow over (v)}≦γ _(vel)

{square root}{square root over (a)}∘{right arrow over (a)}≦γ _(acc)

[0087] where {right arrow over (v)}=the two-dimensional orthree-dimensional velocity vector of the target state solution, {rightarrow over (a)}=the two-dimensional or three-dimensional accelerationvector of the target state solution, and γ_(vel), γ_(acc)=targetvelocity and acceleration thresholds, respectively. If either check ofinitialization check function 418 fails, then the new target track maybe deleted and the associated line track may be passed on so it may tryto initialize at the next coherent processing interval update.

[0088] Line track assignment function 420 may apply an assignmentalgorithm to the CACs, sequentially by type. Preferably, the followingorder may be implemented: TL(I,j), LL(I,j) and L(i). The CAC listincludes those that have passed all the gates, such as gates 410 andgates 416. The algorithm may have the order disclosed below. First, linetrack assignment function 420 may make the assignment indicated by theCAC with the lowest score in the list of accepted CAC frominitialization check function 418. Second, line track assignmentfunction 420 may remove from the list those CACs that utilize one ormore of the line tracks assigned above, or that would violate the linkgate because of the new assignment performed above. Third, line trackassignment function 420 may repeat the first and second disclosed stepsuntil all line tracks in the list have been assigned. The preferredorder may be followed in executing these steps in generating assignedline tracks 424. Thus, assigned line tracks 424 may be output from linetrack association function 400.

[0089] Thus, in summary, line track association function 400 gates,scores, and assigns line track by using CACs. The gating operations seekto remove those line tracks that may result in unnecessary processing orerrors. Line track association function 400 scores each CAC using NLSbatch estimator 414. Using the scores, line track association function400 assigns the line tracks appropriately. Preferably, the CAC with thelowest score is assigned first.

[0090]FIG. 5 depicts a flowchart for pre-processing line tracks prior toline track association operations in accordance with an embodiment ofthe present invention. Step 502 executes by performing a modulation linetest. This step determines if the line track under scrutiny is generatedby a blade modulation line, such as a propeller. The test may determineif the delta-delay time-series history obtained from integrated dopplerand from delay differences evolve into a linear runoff. Step 504executes by determining if the line track is generated by a blademodulation line, as disclosed above. If yes, then step 506 executes byremoving the line track. If yes, then step 508 executes by buffering thedetection reports for the line track in a first-in, first out queue.Step 510 executes by marking the line track within the buffer availablefor line track association operations.

[0091]FIG. 6 depicts a flowchart for line track association operationsin accordance with another embodiment of the present invention. Theoperations and steps disclosed with reference to FIG. 6 may correlate tothe features disclosed by FIG. 4. Step 602 executes by selectingappropriate line tracks for association operations. Line tracks may bemarked as available for line track association after passing through abuffer, such as buffer 406. Line tracks that are marked available forline track association preferably have met specified criteria, such asthe modulation line test. Other criteria may include not being assignedto a target track or are being assigned to a target track that does nothave any other line track assignments. Another possible criterion may bethat the line track have a current detection report.

[0092] Step 604 executes by restricting those unassigned line trackshaving no TDOA measurements to the TL(I,j) CAC. Line tracks having noTDOA measurements may be links with television illuminators. Step 606executes by performing illuminator hypothesis operations for channelsthat have identified multiple possible television illuminators. Thefunctions performed in this step are disclosed in greater detail withreference to television frequency line track hypothesis function 408 ofFIG. 4 above.

[0093] Step 608 executes by selecting TL(I,j) CACs for line trackassociation operations. TL(I,j) CACs correlate to those combinationswhere the current track i is combined with line track j. In steps610-620, TL(i,j) CACs may be processed according to embodiments of thepresent invention, as disclosed in greater detail below. Step 610executes by applying pre-scoring gates to the line tracks. Withreference to FIG. 4, gates 410 are applied, and may include a line gate,a normalized innovations squared gate, a two-dimensional position wedgegate, and the like. The line tracks may be removed from furtherconsideration if they do not pass the gates' criteria.

[0094] Step 612 executes by initializing the target tracks using a batchNLS operation. Track initialization data may be provided by this step.The track initialization data may be used when new track are formed orold tracks are reinitialized. Step 614 executes by computing a scorebased on the batch fit of the initialized target tracks. An initialstate vector and a covariance matrix may be estimated based on thebuffered detection reports from one or more line tracks. Scorecomputation operations are disclosed in greater detail below.

[0095] Step 616 executes by applying post-scoring gates to the scoredCACs, such as gates 416. The post-scoring gates may reject those CACsthat fail scoring criterion. Step 618 executes by assigning the linetracks based on the CAC scores. As disclosed above, a preferred ordermay be implemented amongst the CACs. According to the disclosedembodiment, this step considers TL(i,j) combinations. The TL(i,j) CACwith the lowest score is assigned. Preferably, only those CACs that havepassed all gates are considered. Step 620 executes by removing thoseCACs that utilize one or more of the line tracks assigned in step 618.Steps 618 and 620 may be repeated until all TL(i,j) combinations areassigned.

[0096] Step 622 executes by selecting LL(i,j) CACs for line trackassociation operations. LL(i,j) CACs correlate to those combinationswhere a new target track is established from line tracks I and j. Insteps 624-634, LL(i,j) CACs may be processed according to embodiments ofthe present invention, as disclosed in greater detail below. Step 624executes by applying pre-scoring gates to the line tracks. Withreference to FIG. 4, gates 410 are applied, and may include a line gate,a normalized innovations squared gate, a two-dimensional position wedgegate, and the like. The line tracks may be removed from furtherconsideration if they do not pass the gates' criteria.

[0097] Step 626 executes by initializing the target tracks using a batchNLS operation. Track initialization data may be provided by this step.The track initialization data may be used when new track are formed orold tracks are reinitialized. Step 628 executes by computing a scorebased on the batch fit of the initialized target tracks. An initialstate vector and a covariance matrix may be estimated based on thebuffered detection reports from one or more line tracks.

[0098] Step 630 executes by applying post-scoring gates to the scoredCACs, such as gates 416. The post-scoring gates may reject those CACsthat fail scoring criterion. Step 632 executes by assigning the linetracks based on the CAC scores. As disclosed above, a preferred ordermay be implemented amongst the CACs. According to the disclosedembodiment, this step considers LL(i,j) combinations. The LL(i,j) CACwith the lowest score is assigned. Preferably, only those CACs that havepassed all gates are considered. Step 634 executes by removing thoseCACs that utilize one or more of the line tracks assigned in step 632.Steps 632 and 634 may be repeated until all TL(i,j) combinations areassigned.

[0099] Step 636 executes by selecting L(i) CACs for line trackassociation operations. L(i) CACs correlate to those combinations wherea new target track i is established from line track i. In steps 638-648,L(i) CACs may be processed according to a disclosed embodiment of thepresent invention. Step 638 executes by applying pre-scoring gates tothe line tracks. With reference to FIG. 4, gates 410 are applied, andmay include a line gate, a normalized innovations squared gate, atwo-dimensional position wedge gate, and the like. The line tracks maybe removed from further consideration if they do not pass the gates'criteria.

[0100] Step 640 executes by initializing the target tracks using a batchNLS operation. Track initialization data may be provided by this step.The track initialization data may be used when new track are formed orold tracks are reinitialized. Step 642 executes by computing a scorebased on the batch fit of the initialized target tracks. An initialstate vector and a covariance matrix may be estimated based on thebuffered detection reports from one or more line tracks.

[0101] Step 644 executes by applying post-scoring gates to the scoredCACs, such as gates 416. The post-scoring gates may reject those CACsthat fail scoring criterion. Step 646 executes by assigning the linetracks based on the CAC scores. As disclosed above, a preferred ordermay be implemented amongst the CACs. According to the disclosedembodiment, this step considers L(i) combinations. The L(i) CAC with thelowest score is assigned. Preferably, those CACs that have passed allgates are considered. Step 648 executes by removing those CACs thatutilize one or more of the line tracks assigned in step 646. Steps 646and 648 may be repeated until all L(i) combinations are assigned.

[0102] Step 650 executes by updating those TL(i,j) combinations thatwere restricted in step 604. These combinations correlate to line tracksfrom links having no TDOA measurements, such as links with televisionilluminators. Now that these CACs are updated, they may be eligible forline track association operations. In steps 652-660, the updated TL(i,j)CACs may be processed according to embodiments of the present invention,as disclosed in greater detail below. Thus, step 652 executes byapplying pre-scoring gates to the line tracks. With reference to FIG. 4,gates 410 are applied, and may include a line gate, a normalizedinnovations squared gate, a two-dimensional position wedge gate, and thelike. The line tracks may be removed from further consideration if theydo not pass the gates' criteria.

[0103] Step 654 executes by initializing the target tracks using a batchNLS operation. Track initialization data may be provided by this step.The track initialization data may be used when new track are formed orold tracks are reinitialized. Step 656 executes by computing a scorebased on the batch fit of the initialized target tracks. An initialstate vector and a covariance matrix may be estimated based on thebuffered detection reports from one or more line tracks.

[0104] Step 658 executes by applying post-scoring gates to the scoredCACs, such as gates 416. The post-scoring gates may reject those CACsthat fail scoring criterion. Step 660 executes by assigning the linetracks based on the CAC scores. As disclosed above, a preferred ordermay be implemented amongst the CACs. According to the disclosedembodiments, this step considers the updated TL(i,j) combinations. Theupdated TL(i,j) CAC with the lowest score is assigned. Preferably, onlythose CACs that have passed all gates are considered. Step 660 may berepeated until all remaining combinations are assigned.

[0105] Although FIG. 6 was disclosed in a specified order, embodimentsof the present invention are not limited by the preferred embodiment.The applying gates, initializing, and scoring steps may be executedsimultaneously with the assigning steps executed according to apreferred order. Further, once the CACs are assigned, the CACspreferably are removed from further target track associationconsideration.

[0106]FIG. 7 depicts a flowchart for initializing and scoring targettracks in accordance with an embodiment of the present invention. Step702 executes by invoking the NLS batch estimator for performing scoringoperations. Step 704 executes by inserting a metric for associating atelevision line track to an existing target track. Preferably,additional operations may be executed for scoring when associating atelevision line track to an existing target track. At the point when theNLS batch estimator, such as NLS batch estimator 414, is queried tocompute a score “s” for a proposed association of a television linetrack with an existing target track, an additional metric may beimplemented. The metric indicates the closeness of the television linetrack to the existing target track via a mean squared residual prior toperforming the NLS fit to compute the score. The mean squared distancemay be computed by:$s_{2} = {\frac{1}{n_{2}}{\sum\limits_{k \in K_{2}}y_{k}^{2}}}$

[0107] where K₂ is the index set of measurements belonging to thetelevision line track under consideration, and n₂ is the number ofmeasurements in K₂. The distance is computed using the normalizedresiduals y_(k) as computed early in the NLS batch fit prior to thefirst iteration.

[0108] Step 706 executes by performing retrace removal operations. Whenthe age of a line track is greater than a threshold η_(age) and thefrequency rate (S_(υ)) is less than the threshold η_(υ), that is inunits of Hz/sec, the line track may be considered to be a retrace lineand is flagged as unusable. The values for the retrace removal analysismay be calculated by:$S_{\upsilon} = {{\alpha \quad S_{\upsilon}} + {\left( {1 - \alpha} \right){\frac{\Delta \quad \upsilon}{\Delta \quad t}}}}$

[0109] where α=e^(−Δt/τ) and

[0110] Δυ=Δ (successive frequency measurements)

[0111] Δt=time step of successive frequency measurements.

[0112] Step 708 executes by seeding the NLS algorithm. The NLS algorithmis an iterative process that may desire an initial seed of the targetstate and state covariance. The initial seed of target position may be apoint computed by the observation-state mapping using the firstdetection in the batch. The initial velocity and accelerations are setto zero. The initial seed of target state covariance may be derived fromthe set of configuration parameters specifying the initial position,velocity, and acceleration standard deviations, or σ_(r),σ_({dot over (r)}), and σ_(a). These relationships may be shown as:$\begin{matrix}{d_{R} = {\frac{1}{2}\frac{{ct}_{d}\left( {{ct}_{d} + {2d_{D}}} \right)}{\left\lbrack {{d_{D}\left( {1 - {\cos \quad \gamma}} \right)} + {ct}_{d}} \right\rbrack}}} \\{x = {d_{R}\quad {\sin \left( {\theta_{ob} + \theta_{bs}} \right)}}} \\{y = {d_{R}\quad {\cos \left( {\theta_{ob} + \theta_{bs}} \right)}}}\end{matrix}$

[0113] where t_(d)=time delay measurement (sec), θ_(ob)=cone anglemeasurement−π/2 (approximate target azimuth relative to boresight inradians), and θ_(bs)=boresight azimuth is radians.

[0114] As disclosed above, the NLS batch estimator is invoked to computea target state and state covariance for the measurements from thedetection reports in the buffer of one or more line tracks. Step 710executes by calculating measurement residuals and partial derivations.For each detection, the measurement residuals and partial derivativesmay be calculated by the following: $\begin{matrix}{y_{k} = {Y_{k} - G_{k}}} \\{{\overset{\sim}{H}}_{k} = {\frac{\partial G_{k}}{\partial X_{T}} = \begin{bmatrix}\frac{\partial G_{k}}{\partial x_{T}} & \frac{\partial G_{k}}{\partial{\overset{.}{x}}_{T}} & \frac{\partial G_{k}}{\partial a_{T}}\end{bmatrix}}}\end{matrix}$

[0115] where Y_(k) is the actual measurement, G_(k) may be the computedmeasurement, and $\frac{\partial G_{k}}{\partial X_{T}}$

[0116] may be the partial derivative expressions developed above.

[0117] Step 712 executes by mapping and scaling partials information.The partial information may be mapped back to the final coherentprocessing interval using

H _(k) ={tilde over (H)} _(k)Φ(t _(k) ,t _(i))

[0118] and scaled according to the measurement standard deviation as$\begin{matrix}{y_{k} = {\frac{1}{\sigma}y_{k}}} \\{H_{k} = {\frac{1}{\sigma}H_{k}}}\end{matrix}$

[0119] Step 714 executes by accumulating measurement residuals andpartial derivatives. The measurement residuals and partial derivativesmay be accumulated into the set of normal equations, such as$\begin{matrix}{M = {\sum\limits_{k = 1}^{n}{H_{k}^{T}H_{k}}}} \\{N = {\sum\limits_{k = 1}^{n}{H_{k}^{T}y_{k}}}}\end{matrix}$

[0120] Step 716 executes by solving the linear system for the correctionto the target state. The correction to the target state may be computedby solving the linear system {circumflex over (X)}=M⁻¹N, where thematrix M⁻¹ is the calculated state covariance for the current iteration.

[0121] The convergence of the estimate may be determined by calculatingthe root-mean-square(“RMS”), the linear predicted root-mean-square(“LPRMS”) and the relative root-mean-square (“RRMS”). The followingssteps disclose the preferred embodiments in calculating the abovevalues. Step 718 executes by calculating the RMS between true andpredicted measurement residuals. The RMS is the square root of the meansquared errors between the true and predicted measurement residuals andmay be calculated as${RMS} = {\sqrt{\frac{\sum\limits_{k = 1}^{n}y_{k}^{2}}{n}}.}$

[0122] Step 720 executes by calculating the LPRMS. The linear predictedRMS may be the estimate of the RMS after the application of the stateupdate. The LPRMS may be calculated according to${LPRMS} = {\sqrt{\frac{{\sum\limits_{k = 1}^{n}y_{k}^{2}} - {\hat{X} \circ N}}{n}}.}$

[0123] Step 722 executes by calculating the RRMS. The relative RMS maybe computed and tested against a user-specified threshold forconvergence according to${RRMS} = {\frac{{R\quad {MS}} - {LPRMS}}{R\quad {MS}} \leq {\varepsilon_{rrms}.}}$

[0124] If the solution has not converged, the state update may be addedto the initial state and the process is repeated until the solutionconverges or the maximum number of iterations, I_(BF), has been reached.

[0125] Step 724 executes by computing the score according to$s = {\frac{{\sum\limits_{k = 1}^{n}\quad y_{k}^{2}} - n}{\sqrt{2n}}.}$

[0126]FIG. 8 depicts a flowchart for assigning line tracks in accordancewith an embodiment of the present invention. As disclosed above, afterscoring, the process for assigning the scored line tracks according to aspecific order. Step 802 executes by receiving the scored CAC list. TheCACs and line tracks should have passed all gates and criteria to beassigned. Step 804 executes by assigning the CAC with the lowest scoreto a target track. Step 806 executes by removing the appropriate CACsfrom consideration that are related to the assigned CAC from step 804.Step 808 executes by determining if all line tracks have been assigned.If no, then the process returns to step 804 to assign the next lowestscoring CAC. If yes, then step 810 executes by assembling the list ofthe assigned line tracks for track filtering operations.

[0127]FIG. 9 depicts a track filtering function in accordance with anembodiment of the present invention. FIG. 9 depicts line trackassociation function 900 that correlates to line track associationfunction 330 of FIG. 3. Track filtering function 900 discloses apreferred embodiment of track filtering function 330. Track filteringfunction 330, however, is not limited by the embodiments disclosed byFIG. 9. Track filtering function 900 may utilize an extended Kalmanfilter to track the position, velocity, and acceleration of each targetin two-dimensions and, if all conditions are met, in three-dimensions.The filter's initial state and covariance are initialized in the linetrack association process disclosed above. Filter tracking may propagateeach track incorporating measurements, and monitor the line trackassociation to ensure it remains valid.

[0128] Detection reports 902 are received at track filtering function900. Detection reports 902 may correlate to detection reports 302.Outlier editing function 904 tests the measurement data from detectionreports 902 to ensure they are statistically consistent with the track,and to ignore those detection reports that fail the test. Outlierediting function 904 may be utilized by computing the normalizedinnovations, or a priori measurement residuals, and comparing the resultto a chi-square distribution threshold. If the threshold is exceeded,the detection report data may be discarded.

[0129] Kalman filter 906 may compute corrections to the state and statecovariance from a sequential stream of detection reports 902. Startingwith the extrapolated target state and state covariance, the algorithmwithin Kalman filter 906 computes states updates using measurementinformation from the current coherent processing interval. The filteringprocess may desire a prediction step and an update step. The predictionstep may desire the computation of measurement residuals and partialderivatives. The filtering operations are disclosed in greater detailbelow.

[0130] The target tracking processing system 300 may permit targets tocoast for a time period after the last line track associated with thetarget has been terminated. As a result, associated targets, or targetscurrently associated with a line track, are passed to the line trackassociation function 320, or 400. Target coasting allows association ofnew line tracks that are reasonable extensions of previously terminatedline tracks to targets without dropping the target track. For each timeupdate, the output 910 from the line track association function 320 isexamined for new targets. The new targets may be compared to the set ofcoasting, or unassociated, targets and merged with the new target ifless that a distance gate. After line track association is performed forthe current time update, coasting target association may be implemented.The process implemented is disclosed in greater detail below.

[0131]FIG. 10 depicts a flowchart for filtering detection reports inaccordance with an embodiment of the present invention. Step 1002executes by retrieving measurements for the current coherent processinginterval. For each measurement in the current coherent processinginterval, the values may be $\begin{matrix}{y_{k} = {Y_{k} - G_{k}}} \\{H_{k} = {\frac{\partial G_{k}}{\partial X_{T}} = \left\lbrack \begin{matrix}\frac{\partial G_{k}}{\partial x_{T}} & \frac{\partial G_{k}}{\partial{\overset{.}{x}}_{T}} & \left. \frac{\partial G_{k}}{\partial a_{T}} \right\rbrack\end{matrix} \right.}}\end{matrix}$

[0132] where Y_(k) is the actual measurement, G_(k) is the computedmeasurement and $\frac{\partial G_{k}}{\partial X_{T}}$

[0133] are the partial derivative expressions developed above.

[0134] Step 1004 executes by updating the state and state covariance.The updating operation to correct the state and state covariance may be

P=(I−KH){overscore (P)}(I−KH)^(T) +KRK ^(T)

X={overscore (X)}+Ky

[0135] where {overscore (P)} may be the extrapolated state covariancematrix, H may be the measurement partial matrix. The Kalman gain may begiven by K={overscore (P)}H^(T)S⁻¹ and the measurement predictedresidual may be given by S=H{overscore (P)}H^(T)+R, where R is themeasurement noise matrix. If the target track may be a single linktrack, scale the doppler variance value in the measurement noise matrix,R, by the doppler variance scale factor, K_(σ_(f_(d))²),

[0136] , before computing the Kalman gain.

[0137] Step 1006 executed by adding independent weight factors.Independent weighting factors may be added for measurements, by type, inthe location tracking filter, such as for building the “R” measurementcovariance matrix. For example, $R = \begin{bmatrix}\left( {C_{\tau}\sigma_{\tau}} \right)^{2} & 0 & 0 & 0 \\0 & \left( {C_{\upsilon}\sigma_{\upsilon}} \right)^{2} & 0 & 0 \\0 & 0 & \left( {C_{\theta}\sigma_{\theta}} \right)^{2} & 0 \\0 & 0 & 0 & \left( {C_{\varphi}\sigma_{\varphi}} \right)^{2}\end{bmatrix}$

[0138] where C_(τ), C_(ν), C_(θ), and C_(φ) may be the weighting factorsin unitless configuration parameters for delay, doppler, azimuth, andelevation measurement types. Preferably, other functions, such as linetracking, line track association, and the like, use the variancesspecified within the detection reports.

[0139] Whenever a detection report contains τ, σ_(τ) ², ν, σ₈₄ ², θ,σ_(θ) ², φ, and σ₁₀₀ ², which are the measurements and their variances,within the detector, there may be a scale factor for variances toaccount for windowing, small biases, and the like. For example,$\sigma_{\tau}^{2} = {\frac{K_{\tau}^{2}}{{SNR} \cdot \beta^{2}}.}$

[0140] Step 1008 executes by establishing minimum bounds for the stateerror variances. The state error covariance matrix after the measurementupdate maybe given by

[0141] P≡[Pij] where I=1, 2, . . . , N; j=1, 2, . . . , N and

[0142] (σ_(I))_(min)≡minimum sigma for state I, which may be aconfigurable parameter. After a measurement update, P may be enforced tosatisfy the minimum values of σ. If {square root}P_(ii)<(σ_(i))_(min),then set $a = \frac{\left( \sigma_{i} \right)_{\min}}{\sqrt{P_{ii}}}$

[0143] and set P_(ij)=a P_(ij), P_(ji)=aP_(ji) for j=1, 2, . . . , N.

[0144] Step 1010 executes by limiting the altitude step size betweentarget state updates. The altitude step size may be limited betweentarget state updates by checking the filter state update valuecorresponding to the target's “Up” component of the ENU coordinatesystem. If |U_(t) _(i) −U_(t) _(i−1) |>γ_(U) _(step) , then setU_(ti)=U_(ti−1)+γ_(zstep) or U_(ti)=U_(ti−1)−γ_(Ustep) depending on thesign of the difference, where z_(ti)=the Up component of the target'sposition at the current update time, z_(ti−1)=the Up component of thetarget's position at the previous update time, and γ_(Ustep)=maximum Upstep size for target state updates.

[0145] Step 1012 executes by computing the log-likelihood function. Thelog-likelihood function may be computed at each coherent processinginterval according to λ_(k)=λ_(k−1)+y_(k) ^(T)S⁻¹y_(k). Step 1014executes by normalizing the log-likelihood function. The normalizedlog-likelihood function may be computed as${{\overset{\_}{\lambda}}_{k} = \frac{\lambda_{k} - n}{\sqrt{2n}}},$

[0146] where n is the total number of observations incorporated intoλ_(k).

[0147] Step 1016 executes by performing a dissociation analysis. If anFM line track age has reached or exceeded a specified level, a testknown to one skilled in the art may be applied using a second specifiedthreshold. If the FM line track fails this test, it may be tagged as amodulation line track, and, if it is part of a location track, the trackmay be terminated.

[0148] For example, if a target track's age is greater than aconfigurable time, period, T_(age)>τ_(llh), where τ_(llh)=minimum targetage before performing log-likelihood test, then the normalizedlog-likelihood may be compared to a threshold, or {overscore(λ)}_(k)≦γ_(llh). If outside the threshold, the line track may bedissociated as disclosed below.

[0149] Operations may be implemented when the log-likelihood test failsfor a location track composed of multiple FM and television line tracks.The assumptions for the line tracks are that a FM line track is composedof detection reports containing delay, doppler, and an angle-of-arrivalmeasurement, and a television line track is composed of detectionreports containing doppler measurements only. Signal power andsignal-to-noise ratio estimates also may be included in detectionreports. A FM line track without an angle may be treated as a televisionline track. This action may be different from the dissociation actionthat may be taken when a FM line track that is assumed to be part of alocation track is declared to be a FM modulation line.

[0150] The following guidelines describe the dissociation logic. Forconvenience, “track” may refer to the location track in question, and“test” may refer to the log-likelihood test. If the track is composed ofa single FM line track and one or more television line tracks, then,regardless of which line track failed the test, the track may bereinitialized using the FM line track and discarding the television linetracks. If the track is composed of two or more FM line tracks and anynumber of television line tracks, the line track failing the test may bediscarded. The track may be reinitialized if the track was composed oftwo FM line tracks and no television line tracks. If the track iscomposed of television line tracks, the track may be terminated.

[0151] Step 1018 executes by performing validity checks to ensure thetarget is valid. This step is disclosed in greater detail below. Step1020 executes by correcting the vertical components of the updatedtarget's position and velocity. For two-dimensional target tracks, thevertical components may be corrected. Preferably, the verticalcomponents of the updated target's position and velocity are updated, orx_(U) and x_(U) _(²) , to account for the earth's curvature.

[0152]FIG. 11 depicts a flowchart for performing validity checks fortargets in accordance with an embodiment of the present invention. FIG.11 correlates to step 1018 of FIG. 10. Step 1018, however, is notlimited by the embodiments disclosed by FIG. 11. Step 1102 executes bycomparing the horizontal position step update. For all targets, thehorizontal position step update may be checked against a distancethreshold by letting {circumflex over (X)}=Ky from above be the computedupdate to the target state vector for the current coherent positioninterval. Let {circumflex over (X)}_(p) ^(T)=[{circumflex over (x)} ŷ{circumflex over (z)}] be the position vector form the target statevector. The horizontal position step may be checked as

{square root}{square root over ({circumflex over (x)})} ² +ŷ ²≦γ_(pos)

[0153] where γ_(pos)=target position step threshold. Step 1104 executesby determining whether the position check passed. If no, then step 1106executes by updating the target state vector for position approximatelyequal to the target position threshold. If yes, then step 1108 isexecuted.

[0154] Step 1108 executes by computing the velocity magnitude of theinitial state solution. Preferably, for all targets, the velocitymagnitude of the initial state solution may be computed. Step 1110executes by comparing the velocity magnitude to a velocity threshold as

{square root}{square root over (v)}∘{right arrow over (v)}≦γ _(vel)

[0155] where {right arrow over (v)}=the two-dimensional orthree-dimensional velocity vector of the target state solution, andγ_(vel)=the target velocity threshold. Step 1112 executes by determiningwhether the velocity check passed for the target. If no, then step 1114executes by deleting the target track and passing its associated linetrack on so it may try to initialize at the next coherent processinginterval update. Alternatively, the target track may be reinitializedusing the currently associated line tracks. A configuration switch,M_(vel) _(—) _(acc), may dictate which operation is executed if thecheck fails.

[0156] If step 1112 is yes, then step 1116 executes by computing theacceleration magnitude of the initial state solution. Preferably, forall targets, the acceleration magnitude of the initial state solutionmay be computed. Step 1118 executes by comparing the accelerationmagnitude to an acceleration threshold as

{square root}{square root over (a)}∘{right arrow over (a)}≦γ _(acc)

[0157] where {right arrow over (a)}=the two-dimensional orthree-dimensional acceleration vector of the target state solution, andγ_(acc)=the target acceleration threshold. Step 1120 executes bydetermining whether the acceleration check passed for the target. If no,then step 1124 executes by deleting the target track and passing itsassociated line track on so it may try to initialize at the nextcoherent processing interval update. Alternatively, the target track maybe reinitialized using the currently associated line tracks. Aconfiguration switch, M_(vel) _(—) _(acc), may dictate which operationis executed if the check fails. If step 1120 is yes, then step 1122executes by indicating that target is valid, and may be passed fortarget extrapolation or further processing.

[0158]FIG. 12 depicts a flowchart for associating a coasting target inaccordance with an embodiment of the present invention. Target trackingprocessing system, such as system 300, incorporating a coasting targetassociation function, such coasting target association function 908, maypermit targets to coast for a configurable time period after the lastline track associated with the target is terminated. As a result,associated targets, or targets currently associated with a line track,may be passed to the line track association function, such as line trackassociation function 320. Coasting targets allow association of new linetracks that are reasonable extensions of previously terminated linetracks to targets without dropping the target track. For each timeupdate, the line track association function output may be examined fornew targets. The new targets may be compared to the set of coasting, orunassociated, targets and merged with the new target if less than adistance gate. After line track association is performed for the currenttime update, the coasting target may be implemented. Although disclosedas an element of the track filtering function 900 of FIG. 9, thecoasting target association function may be implemented outside thetrack filtering function, and configured anyplace within target trackingprocessing system 300. The coasting target operations disclosed belowmay be for each unassociated target in the current coherent processinginterval.

[0159] Step 1202 executes by checking to see if the allowable coastingperiod has expired, or (t_(current)−t_(coast))>τ_(coast). Step 1204executes by determining whether the coasting period has expired. If yes,then step 1206 executes by terminating the target. Step 1208 executes bygoing to the next unassociated target in the current coherent processinginterval.

[0160] If step 1204 is no, then step 1210 executes by performing avelocity check. Preferably, for all targets, the velocity magnitude ofthe initial state solution may be computed and compared to a velocitythreshold as {square root}{square root over (v)}∘{right arrow over(v)}≦γ_(vel), where {right arrow over (v)}=the two-dimensional or threedimensional velocity vector of the target state solution, andγ_(vel)=the target velocity threshold. If the velocity check fails, theneither delete the target track and pass its associated line track on soit may try to initialize at the next coherent processing intervalupdate, or reinitialize the target using the currently associated linetracks. A configuration switch, M_(vel) _(—) _(acc), may dictate whichmethod is carried out if the check fails.

[0161] If the velocity check is valid, then step 1212 executes byperforming an acceleration check. Preferably, for all targets, theacceleration magnitude of the initial state solution may compute andcompared to acceleration threshold as {square root}{square root over(a)}∘{right arrow over (a)}≦γ_(acc) where {right arrow over (a)}=thetwo-dimensional or three-dimensional acceleration vector of the targetstate solution, and γ_(acc)=target acceleration threshold. If theacceleration check fails, then either delete the target track and passits associated line track on so it may try to initialize at the nextcoherent processing interval update, or reinitialize the target usingthe currently associated line tracks. The configuration switch, M_(vel)_(—) _(acc), may dictate which method is carried out if the check fails.

[0162] Step 1214 executes by computing the predicted measurements oftime delay, doppler, and angle-of-arrival based on the current targetstate prediction for the unassociated target. The set of new targetsreported by the line track association function may be looped over forthe current coherent processing interval. Step 1216 executes bycomparing the new target and the unassociated, or coasting, targetmeasurements of delay, doppler, and angle-of-arrival by $\begin{matrix}{{\left( {t_{d_{coast}} - t_{d_{new}}} \right)^{2} < {\left( \frac{100}{{SNR}_{new}} \right)\left( \gamma_{t_{coast}} \right)^{2}}}\&\&} \\{{\left( {f_{d_{coast}} - f_{d_{new}}} \right)^{2} < {\left( \frac{100}{{SNR}_{new}} \right)\left( \gamma_{f_{coast}} \right)^{2}}}\&\&} \\{\left( {\theta_{coast} - \theta_{new}} \right)^{2} < {\left( \frac{100}{{SNR}_{new}} \right)\left( \gamma_{\theta_{coast}} \right)^{2}}}\end{matrix}$

[0163] where t_(d) _(new) , f_(d) _(new) , θ_(new) are the latestdetected measurements for the new target, and γ_(t) _(coast) , γ_(f)_(coast) , γ_(θ) _(coast) are the coasting target measurement gates. Inaddition, a comparison on the target bearings may be made, or

(Bearing_(cost)−Bearing_(new))²<(γ_(B) _(coast) )²

[0164] where γ_(B) _(coast) =a coast target bearing gate. Step 1218executes by determining whether the compared new target and theunassociated target meet the above disclosed conditions. If no, thenstep 1220 executes by going to the next unassociated target. If yes,then step 1222 executes by adding those pairs of new targets andunassociated targets to a list of possible coasting target associations.

[0165] Step 1224 executes by checking the virtual coast flag. Subsequentoperations may depend on the state of a virtual coast flag. A virtualcoast may attempt to maintain target identification continuity when newtargets have been associated with coasting targets. Step 1226 executesby determining whether the virtual coast flag is true. If yes, then step1228 executes by extracting the target identification from the coastingtarget. Step 1230 executes by terminating the coasting target. Step 1232executes by resetting the target identification of the new target tothat of the coasting target while maintaining continuity with all datapreviously associated with the coasting target, such as file outputs,burned-in tracks on screen, and the like. If step 1226 is no, then step1234 executes by dissociating the new target's line tracks andassociating them with the unassociated target. Step 1236 executes byterminating the new target track.

[0166] Thus, in accordance with the disclosed embodiments, a system andmethod for central association and tracking for PCL applications isdisclosed. The disclosed embodiments receive detection reports and otherinformation as inputs and associates the detection reports with existingline tracks, creates new line tracks or terminates line tracks accordingto the data within the detection reports. The detection reports containdata for signals reflected from potential targets that are being trackedby the PCL system. The disclosed methods, processes and algorithmsimprove target track estimation techniques. Position, velocity, andacceleration may be estimated for targets detected by the PCL system.Target track processing may be improved by the disclosed embodiments.Therefore, targets may be identified and tracked in a more efficientmanner.

[0167] In accordance with the disclosed embodiments, detection reportsmay be received at a target tracking processing system. The detectionreports include measurements correlating to line tracks associated withtarget echoes in earlier processing operations. In addition, otherinformation, such as parameters and observables, may be received by thetarget track processing system. The target tracking processing systemmay perform a line track association function and a track filteringfunction on the line tracks according to the measurements within thedetection reports. The line track association function scores CACs ofthe line tracks and assigns the line tracks according to the scores totarget tracks. The line track association function also may initializenew target tracks according to the line tracks. The track filteringfunction may test and propagated the target tracks according to thereceived measurements within the detection reports. These operationsalso help in predicting and estimating target parameters for tracking.Target parameters may be extrapolated from the propagated and updatedtarget tracks, and fed to a display for a user, or back into the PCLsystem for further processing.

[0168] It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodiments ofthe present invention without departing from the spirit or scope of theinvention. Thus, it is intended that the present invention embodies themodifications and variations of this invention provided that they comewithin the scope of the appended claims and their equivalents.

What is claimed is:
 1. A method for associating a line track with atarget for a passive coherent location system, comprising: receiving adetection report having said line track corresponding to said target;computing a target state using measurements of said line track;computing a state covariance using said measurement of said line track;scoring said line track according to said target state and said statecovariance; and assigning said line track to a target track according tosaid scoring.